On a parameter extention for q-divergence-based fuzzy clustering

Abstract

This study proposes a fuzzy clustering algorithm for vectotial data, which is constructed by extending the fuzzification parameters in the q-divergence-based fuzzy c-means algorithm (QFCM). The proposed algorithm, extended QFCM (eQFCM), is an extension QFCM and the penalized fuzzy c-means algorithm proposed by Yang, referred to as Y-type FCM (FCM). eQFCM extends both the two-parameter QFCM and Y-FCM algorithms to a four-parameter model. Through numerical experiments using an artificial dataset, the theoretical discussion is substantiated and some numerical experiment using real datasets show that the proposed algorithm outperforms conventional ones in terms of clustering accuracy.